arXiv: cs.AI preprintsInternational6 November 2026
DynSHAP: Towards Explainable Dynamic Survival Analysis
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arXiv:2609.13042v2 Announce Type: replace-cross Abstract: Deep learning models for dynamic survival analysis (DSA) achieve strong predictive performance by incorporating longitudinal patient data, but their black box nature limits clinical trust and adoption. Existing explainability methods cannot handle longitudinal, irregular inputs and functional survival outputs simultaneously, which limits their usability in DSA. We propose DynSHAP, a SHAP framework suited specifically for dynamic survival analysis. It extends common marginal SHAP estimators to this setting by treating time--feature pairs
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